Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx skills add bigbio/sdrf-skills --skill sdrf-templatesgit clone --depth 1 https://github.com/bigbio/sdrf-skillsWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/bigbio/sdrf-skills/sdrf-templates)<a href="https://agentmods.dev/skills/bigbio/sdrf-skills/sdrf-templates"><img src="https://agentmods.dev/badge/skills/bigbio/sdrf-skills/sdrf-templates/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/bigbio/sdrf-skills/sdrf-templates"><img src="https://agentmods.dev/badge/skills/bigbio/sdrf-skills/sdrf-templates.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector pass
What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00041 | $0.02394 |
| Opus 5 | $0.00020 | $0.01197 |
| Sonnet 5 | $0.00008 | $0.00479 |
| Haiku 4.5 | $0.00004 | $0.00239 |
Grade A, and why
sdrf-templates scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 4d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
How it starts
The opening of the file, as written. The whole thing — 179 lines — stays where its author put it; the contents beside it link to each section on GitHub.
SDRF Template System
Templates define which columns are required for a given experiment type.
Each SDRF can declare one or more templates via comment[sdrf template] columns.
Specification Data (always read from source)
The authoritative source for all template information is in the spec/ submodule:
- Template manifest: Read
spec/sdrf-proteomics/sdrf-templates/templates.yaml - Individual templates: Read
spec/sdrf-proteomics/sdrf-templates/{name}/{version}/{name}.yaml - Column definitions: Read
spec/sdrf-proteomics/TERMS.tsv(theusagefield shows which templates include each column)
Always read templates.yaml when answering questions about templates, versions, inheritance,
or mutual exclusivity. Never rely on memorized template data — the spec evolves.
How to Read templates.yaml
The manifest file lists every template with these fields:
name— template identifier (e.g.,ms-proteomics,human)version— current version (e.g.,1.1.0)extends— parent template with version constraint (e.g.,sample-metadata@>=1.0.0)description— what the template addsusable_alone— whether it can be used without other templates (onlyms-proteomicsandaffinity-proteomics)excludes— templates that are mutually exclusive with this onelayer— which selection layer it belongs to
How to Read Individual Template YAMLs
Each template has a YAML file at spec/sdrf-proteomics/sdrf-templates/{name}/{version}/{name}.yaml.
These define the columns the template adds, with requirement levels (required/recommended/optional).
How to Find Columns for a Template
Two ways:
- Read the individual template YAML → lists columns with requirement levels
- Read TERMS.tsv → filter rows where
usagecontains the template name
Template Layers (Methodology — stable across versions)
Templates are organized into layers. Each layer serves a different purpose:
- Technology (REQUIRED — pick exactly one): The measurement technology used.
ms-proteomics— mass spectrometry experimentsaffinity-proteomics— Olink, SomaScan, and other affinity platforms- These are mutually exclusive
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 4d ago Changed 1e2f5f706a8e
- 11d ago First seen · 179 lines · 41 tokens per session scan A a206d707cbaa
sdrf-templates is a skill published in the GitHub repository bigbio/sdrf-skills (18 stars, last pushed 4d ago), licensed MIT. It adds 41 tokens to every session and 2,394 once invoked, about $0.0002 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.
Other skills, from other repositories
uniprot-query
Query UniProt database for protein sequences, metadata, and search by criteria. Use this skill when: (1) Looking up protein information by UniProt accession ID, (2) Searching proteins by gene name, organism, function, or disease, (3) Retrieving comprehensive protein metadata including domains, PTMs, and annotations.
proteomics-de
Load when computing two-group differential protein abundance (group2 vs group1, log2FC + p-value + BH-adjusted FDR) via Welch t-test, equal-variance t-test, or Mann-Whitney on a wide protein × sample CSV. Skip when you need multi-condition DE (run pairwise contrasts manually); label-based TMT linear-mixed models.
proteomics-enrichment
Load when running over-representation analysis (ORA) on a list of proteins via Fisher's exact test against a built-in 8-pathway DEMO dictionary, with BH-FDR correction. Skip when needing a real pathway database (this skill is demo-only) (use bulkrna-enrichment); rank-based GSEA.
proteomics-ptm
Load when summarising PTM sites (phosphorylation, acetylation, ubiquitination, etc.) from a per-site CSV — site-class assignment (Olsen et al. Class I/II/III by localizationprobability), per-PTM-type counts, amino-acid distribution, sites-per-protein. Skip when raw spectra are the input; you only need protein-level…
proteomics-quantification
Load when computing per-protein abundance from a peptide / PSM table via LFQ (intensity summation), iBAQ (intensity / tryptic peptide count), or spectral counting (PSMs per protein). Skip when the input is already protein-level (use proteomics-ms-qc); label-based TMT / iTRAQ workflows (search upstream first).
proteomics-structural
Load when summarising cross-linking MS (XL-MS) results — intra/inter-protein link split, optional FDR filtering, distance-constraint validation against a per-crosslinker (DSS / BS3 / EDC / DSSO / DSBU) max distance. Skip when raw spectra are the input (run XlinkX / pLink / xiSEARCH first); no XL-MS experiment was…